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Found 2 papers, 1 papers with code
DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networks
It reduces the cost of labeling by prioritizing the selection of test inputs with high fault revealing power from large unlabeled datasets.
Black-Box Testing of Deep Neural Networks Through Test Case Diversity
In this paper, we investigate black-box input diversity metrics as an alternative to white-box coverage criteria.
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